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Quantitative Analysis of Forthcoming ICC Men’s T20 World Cup 2020 Winner Prediction using Machine Learning

by Shashank Singh, Yash Aggarwal, Kumud Kundu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 32
Year of Publication: 2020
Authors: Shashank Singh, Yash Aggarwal, Kumud Kundu
10.5120/ijca2020920388

Shashank Singh, Yash Aggarwal, Kumud Kundu . Quantitative Analysis of Forthcoming ICC Men’s T20 World Cup 2020 Winner Prediction using Machine Learning. International Journal of Computer Applications. 176, 32 ( Jun 2020), 46-51. DOI=10.5120/ijca2020920388

@article{ 10.5120/ijca2020920388,
author = { Shashank Singh, Yash Aggarwal, Kumud Kundu },
title = { Quantitative Analysis of Forthcoming ICC Men’s T20 World Cup 2020 Winner Prediction using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 32 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 46-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number32/31412-2020920388/ },
doi = { 10.5120/ijca2020920388 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:05.633310+05:30
%A Shashank Singh
%A Yash Aggarwal
%A Kumud Kundu
%T Quantitative Analysis of Forthcoming ICC Men’s T20 World Cup 2020 Winner Prediction using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 32
%P 46-51
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The ICC Men’s T20 Cricket World Cup 2020 is scheduled to be hosted by Australia in the month of October and November 2020. Machine Learning in sports analytics is now a days actively applied for prediction of winners. The work presented in this paper aims to predict the winner of the upcoming seventh version of ICC Men’s T20 world cup using Random Forest Classifier, Naïve Bayes, KNN, Logistic Regression, Decision Tree, SVM, Bagging Classifier, Extra Trees Classifier, Voting (HARD & SOFT) training. All these approaches are tested on the different available historic data of international cricket matches played between different countries from 2005 to March 2020. Unstructured historic cricket statistics is picked from ESPN and Cricbuzz websites. Experimental results prove that all approaches are able to imbibe the extracted patterns from the various set of matches performed and hence is found suitable to predict the winner of the ICC Men’s T20 Cricket World Cup 2020. A comparative study is also presented for the predictions made through different approaches.

References
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  9. ]https://stats.espncricinfo.com/ci/engine/records/index.html?id=89;type=trophy.
  10. ]https://www.cricbuzz.com/cricket-series/2798/icc-mens-t20-world-cup 2020/stats#!/?statsType=mostRuns&seriesType=WCT20&seriesId=2798.
  11. https://www.cricbuzz.com/cricket-series/2798/icc-mens-t20-world-cup-2020/matches.
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Index Terms

Computer Science
Information Sciences

Keywords

Cricket analytics Winner Prediction Classification.

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